Module 3: Terrain Visualization

 For this week's lab, we explored different approaches to visualizing terrain information. We focused mainly on raster-based data and creating data from the rasters, i.e.: Digital Elevation Model (DEM), Triangulated Irregular Network (TIN), and then contours created from the DEM data. This lab also focused on hill shading, mainly to portray elevation data accurately. Our figure deliverable for this week's lab is a landcover map of Yellowstone National Park. The goal was to accurately display the land types and the elevation within the park. Here is the resulting map: 

My goal was to give this map a feeling that it came straight from the National Park Service, so I used scales, a north arrow, and fonts that either came straight from their styles or were closely related to it. For the colors of the land types, I wanted to use colors that closely related to their leaf colors, for example, aspen being orange for the color of the aspen tree's leaf. The other trees however are similarly all green, so I tried going with greenish colors that didn't contrast with each other. At my job, I do a lot of land cover type maps, and almost always nonforested areas are made into a neutral color that easily makes it clear that it is an outlier compared to the other land types. So, for this map, I made the non-forested areas a neutral light grayish brown. To make the map pop out more to the reader so that they know where to focus, I gave the map a sort of 3-D effect. Due to the park's awkward dimensions, I thought a landscape map would be best. This made it easier to fill in white space with the essential map elements while also making clear what the focus of the map is for the viewer.

Module 2: Coordinate Systems

 In this week's lab, I got to explore several different coordinate systems and projections. By completing the lab I can understand the nature of distortions that occur due to projections, and as a result, be able to select the best projection for any study area I choose. While we explored many different types of projects, the deliverable for the lab required us to select an area of interest and decide what coordinate system was best for that area of interest. I decided to go with the state of Maine. While Maine does have a state plane coordinate system, it wouldn't be best to showcase the state equally because it is divided into two coordinate systems, east and west. So the next bet would be a state system, a coordinate system representing an entire state. The problem I ran into though was that Maine does not have a state system. So my only other option would be UTM. Maine falls directly in the NAD 1983 UTM Zone 20N, which is perfect. UTM is the ideal system for regions with a smaller east-west extent compared to the north-south extent, and Maine perfectly fits that description. Maine is also relatively small in extent and well contained within zone 20N, as a result, the distortion that typically occurs with projections is minimized drastically in scale and measurements. Here is how the map turned out:

New Class, New Semester, More Maps - Module 1: Map Design & Typography

 In this Lab, I was tasked with exploring symbology, cartographic tools, and general map design. In total, we were tasked with making five different maps. I really enjoyed this lab because it helped me think creatively about how I want to design my maps in a way that meets the criteria of the five map design principles. In case you don't know what they are, it is visual contrast, legibility, figure-ground organization, hierarchical organization, and balance. When it comes to making maps for my career, visual contrast is sometimes something I struggle with because no matter what color I choose it can still look like the colors aren't contrasting well enough (I promise I'm not color-blind). One of these design principles I want to focus on next is legibility. Legibility is very important for maps, especially for this map:

One key to having a legible map is the typography choices you make. For this map, I used varying font sizes to reflect the importance of some features. For example, Mexico City has a smaller font size compared to the countries being labeled. Another choice I made for typography was the text placement. I made sure to minimize overlap as much as possible by converting my river labels to annotations and manually placing a curved label. Overall, these small choices make the map far more legible than it was with a dynamic label placement.